The present invention relates generally to systems and methods for determination of patient true state using automated first pass review of patient medical records. Knowledge of the true state of a patient (determination of patient condition) enables management of coding risks, as well as enhanced patient management and record retention abilities. Some embodiments of the present systems and methods enable more accurate and rapid capture of MediCare eligible conditions, thereby ensuring providers are more fairly compensated, and ensure that medical records more accurately reflect a patient's condition.
Despite rapid growth of innovation in other fields in recent decades, the world of medical information, including patient medical records, billing, referrals, and a host of other information, has enjoyed little to no useful consolidation, reliability, or ease-of-access, leaving medical professionals, hospitals, clinics, and even insurance companies with many issues, such as unreliability of medical information, uncertainty of diagnosis, lack of standard, and a slew of other related problems.
One common problem with the analysis of medical records is that identification of clinically pertinent conditions is often not properly identified, and further, even when identified, the evidence in the patient records to support such a finding is not always properly referenced. Moreover, the process for verifying a condition is often time consuming and labor intensive. This results in a few issues, including: MediCare compensation difficulties, missing of important health conditions and/or misdiagnosis, and lastly the clouding of medical analytics with incomplete or incorrect data.
The first issue, compensation by MediCare, results in providers being underpaid for work performed. This may cause many providers to shy away from MediCare patients, increases cost on other patients, and generally leads to inefficiencies in the administration of government backed medical coverage. Additionally, miss-coding of MediCare claim opens providers to potential audit risk.
The second issue, improper or incomplete diagnosis, can be extremely detrimental to the patient. Often early treatment of a condition results in a far better prognosis for the patient. In the extreme, delays of treatment may reduce the patient's life expectancy. As such, there is a very compelling reason to ensure the medical information of a patient is properly documented, with a high degree of accuracy.
In addition to these direct health impacts to the patient, improper or incomplete diagnosis of the patient can lead to unnecessary tests or follow-ups, which can be financially taxing as well as a drain on the resources of the medical community. Thus there are also tangible financial implications to proper diagnosis with supporting evidence.
Lastly, incorrect or missing data may result in the skewing of analytics performed using the medical records. The medical community is entering into an age of big data analysis. These analyses of large data sets of aggregated medical records generated best practices and means for refining a medical practice. It also enables early detection of health trends and patient behavior. Using these results, medical professionals have the opportunity to greatly increase the efficiency of the administration of medical services. This translates directly into improved patient care at reduced costs. However, such analysis relies upon datasets that are accurate. When the input data is flawed, or incomplete, the analysis suffers.
It is therefore apparent that an urgent need exists for improved means for recordation and analysis of medical records. In particular, the clinical state of patients may be determined using a computerized system, which then enables a host of subsequent activities, including: 1) enhanced personalized medicine, 2) coding audit risk management, 3) more complete and accurate record keeping for providers, and 4) MediCare reimbursement optimization via the identification of coding opportunities.